To install:

devtools::install_github("dtm2451/dittoSeq")

# For older versions:
#   Old DB plotter version
# devtools::install_github("dtm2451/dittoSeq@v0.2")
#   Old DB plotter version plus some early ditto plotters
# devtools::install_github("dtm2451/dittoSeq@v0.2.20")

News: version 0.3.0 is LIVE

Includes lots of new features!

Compatibility with bulk RNAseq data that was processed with edgeR & limma-voom.
dittoScatterPlot() which allows plotting of gene x gene / metadata x metadata / gene x metadata. Great for examining raw droplet data QC, or potential marker gene RNA or CITE-seq expression.
dittoDimPlot now supports overlay of pseudotime-analysis trajectory paths.
Retrieval of underlying data as a dataframe or matrix. Just add data.out = TRUE to the call.
Many more!

Other updates since version 0.2:

Documentation overhaul for most functions with plenty of example code added.
The package is now camelCase, dittoSeq, to go along with typical conventions.
Visualization names all start with ditto... and multi_ditto... instead of DB... and multiDB... Example: dittoDimPlot and multi_dittoDimPlot.
Color Storage: The colors are now retrievable with a simple, empty, function call, dittoColors(). For use outside of dittoSeq, simply use dittoColors(). Example within dittoSeq: dittoDimPlot("ident", seurat, color.panel = dittoColors() ).

More detailed vignettes are planned, but in the meantime, if the in-R documentation (example: ?dittoScatterPlot) and examples are not enough, please create an issue asking how to make whatever plot you would like!

Color blindness friendliness:

The default colors of this package are meant to be color blind friendly. To make it so, I used the suggested colors from this source: Wong B, “Points of view: Color blindness.” Nature Methods, 2011 and adapted them slightly by appending darker and lighter versions to create a 24 color vector. All plotting functions use these colors, stored in dittoColors(), by default. Also included is a Simulate() function that allows you to see what your function might look like to a colorblind individual. For more info on that, see my Colorblindness Compatibility Page

Quick Start Guide:

(For rendered plots, download and open Vignette/QuickStartRender.html)

# Install
devtools::install_github("dtm2451/dittoSeq@Development-v0.3.0")

## Skipping install of 'dittoSeq' from a github remote, the SHA1 (94a3666e) has not changed since last install.
##   Use `force = TRUE` to force installation

# (Be sure to restart after a re-install!)

Load in your data, then go!:

library(dittoSeq)

## Loading required package: ggplot2

# library(Seurat)

# For working with scRNAseq data, works directly with Seurat and SingleCellExperiment objects
seurat <- Seurat::pbmc_small

## Registered S3 method overwritten by 'R.oo':
##   method        from       
##   throw.default R.methodsS3

dittoPlot("CD14", seurat, group.by = "ident")

sce <- Seurat::as.SingleCellExperiment(seurat)
dittoBarPlot("ident", sce, group.by = "RNA_snn_res.0.8")

# For working with bulk RNAseq data, first load your data into a format that dittoSeq quickly understands
# deseq2 <- importDESeq2()
# edger <- importEdgeR()
# limma.voom <- importEdgeR()
myRNA <- RNAseq_mock
dittoDimPlot("Gene1", myRNA, size = 3)

Quickly determine the metadata and gene options for plotting with helper functions:

get.metas(seurat)

## [1] "orig.ident"      "nCount_RNA"      "nFeature_RNA"    "RNA_snn_res.0.8"
## [5] "letter.idents"   "groups"          "RNA_snn_res.1"

is.meta("nCount_RNA", seurat)

## [1] TRUE

get.genes(myRNA)

##   [1] "Gene1"   "Gene2"   "Gene3"   "Gene4"   "Gene5"   "Gene6"   "Gene7"  
##   [8] "Gene8"   "Gene9"   "Gene10"  "Gene11"  "Gene12"  "Gene13"  "Gene14" 
##  [15] "Gene15"  "Gene16"  "Gene17"  "Gene18"  "Gene19"  "Gene20"  "Gene21" 
##  [22] "Gene22"  "Gene23"  "Gene24"  "Gene25"  "Gene26"  "Gene27"  "Gene28" 
##  [29] "Gene29"  "Gene30"  "Gene31"  "Gene32"  "Gene33"  "Gene34"  "Gene35" 
##  [36] "Gene36"  "Gene37"  "Gene38"  "Gene39"  "Gene40"  "Gene41"  "Gene42" 
##  [43] "Gene43"  "Gene44"  "Gene45"  "Gene46"  "Gene47"  "Gene48"  "Gene49" 
##  [50] "Gene50"  "Gene51"  "Gene52"  "Gene53"  "Gene54"  "Gene55"  "Gene56" 
##  [57] "Gene57"  "Gene58"  "Gene59"  "Gene60"  "Gene61"  "Gene62"  "Gene63" 
##  [64] "Gene64"  "Gene65"  "Gene66"  "Gene67"  "Gene68"  "Gene69"  "Gene70" 
##  [71] "Gene71"  "Gene72"  "Gene73"  "Gene74"  "Gene75"  "Gene76"  "Gene77" 
##  [78] "Gene78"  "Gene79"  "Gene80"  "Gene81"  "Gene82"  "Gene83"  "Gene84" 
##  [85] "Gene85"  "Gene86"  "Gene87"  "Gene88"  "Gene89"  "Gene90"  "Gene91" 
##  [92] "Gene92"  "Gene93"  "Gene94"  "Gene95"  "Gene96"  "Gene97"  "Gene98" 
##  [99] "Gene99"  "Gene100"

is.gene("CD3E", myRNA)

## [1] FALSE

# View them with these:
gene("CD3E", seurat, data.type = "raw")

## ATGCCAGAACGACT CATGGCCTGTGCAT GAACCTGATGAACC TGACTGGATTCTCA AGTCAGACTGCACA 
##              0              2              1              4              3 
## TCTGATACACGTGT TGGTATCTAAACAG GCAGCTCTGTTTCT GATATAACACGCAT AATGTTGACAGTCA 
##              1              3              4              2              0 
## AGGTCATGAGTGTC AGAGATGATCTCGC GGGTAACTCTAGTG CATGAGACACGGGA TACGCCACTCCGAA 
##              0              0              0              0              0 
## CTAAACCTGTGCAT GTAAGCACTCATTC TTGGTACTGAATCC CATCATACGGAGCA TACATCACGCTAAC 
##              0              0              1              1              1 
## TTACCATGAATCGC ATAGGAGAAACAGA GCGCACGACTTTAC ACTCGCACGAAAGT ATTACCTGCCTTAT 
##              1              0              0              0              0 
## CCCAACTGCAATCG AAATTCGAATCACG CCATCCGATTCGCC TCCACTCTGAGCTT CATCAGGATGCACA 
##              0              0              0              0              0 
## CTAAACCTCTGACA GATAGAGAAGGGTG CTAACGGAACCGAT AGATATACCCGTAA TACTCTGAATCGAC 
##              0              1              0              1              0 
## GCGCATCTTGCTCC GTTGACGATATCGG ACAGGTACTGGTGT GGCATATGCTTATC CATTACACCAACTG 
##              0              0              0              1              0 
## TAGGGACTGAACTC GCTCCATGAGAAGT TACAATGATGCTAG CTTCATGACCGAAT CTGCCAACAGGAGC 
##              1              0              2              0              1 
## TTGCATTGAGCTAC AAGCAAGAGCTTAG CGGCACGAACTCAG GGTGGAGATTACTC GGCCGATGTACTCT 
##              2              0              1              5              2 
## CGTAGCCTGTATGC TGAGCTGAATGCTG CCTATAACGAGACG ATAAGTTGGTACGT AAGCGACTTTGACG 
##              0              0              0              0              0 
## ACCAGTGAATACCG ATTGCACTTGCTTT CTAGGTGATGGTTG GCACTAGACCTTTA CATGCGCTAGTCAC 
##              0              0              0              0              0 
## TTGAGGACTACGCA ATACCACTCTAAGC CATATAGACTAAGC TTTAGCTGTACTCT GACATTCTCCACCT 
##              1              0              0              0              0 
## ACGTGATGCCATGA ATTGTAGATTCCCG GATAGAGATCACGA AATGCGTGGACGGA GCGTAAACACGGTT 
##              0              0              0              0              1 
## ATTCAGCTCATTGG GGCATATGGGGAGT ATCATCTGACACCA GTCATACTTCGCCT TTACGTACGTTCAG 
##              0              0              0              0              0 
## GAGTTGTGGTAGCT GACGCTCTCTCTCG AGTCTTACTTCGGA GGAACACTTCAGAC CTTGATTGATCTTC 
##              0              0              0              1              0

meta("groups", seurat)

##  [1] "g2" "g1" "g2" "g2" "g2" "g1" "g1" "g1" "g1" "g1" "g2" "g1" "g2" "g2"
## [15] "g2" "g1" "g2" "g1" "g1" "g2" "g1" "g1" "g2" "g2" "g1" "g2" "g2" "g2"
## [29] "g2" "g1" "g1" "g1" "g1" "g2" "g1" "g1" "g2" "g1" "g1" "g2" "g1" "g2"
## [43] "g2" "g2" "g1" "g2" "g1" "g2" "g1" "g2" "g1" "g2" "g2" "g2" "g1" "g1"
## [57] "g1" "g1" "g2" "g1" "g1" "g1" "g1" "g1" "g1" "g2" "g2" "g1" "g1" "g1"
## [71] "g2" "g1" "g2" "g2" "g1" "g1" "g2" "g1" "g2" "g1"

meta.levels("groups", seurat)

## [1] "g1" "g2"

There are many dittoSeq Plot Types

Intuitive default adjustments generally allow creation of immediately useable plots.

# dittoPlot
dittoPlot("CD3E", seurat, group.by = "ident")

dittoPlot("CD3E", seurat, group.by = "ident",
    plots = c("boxplot", "jitter"))

dittoPlot("CD3E", seurat, group.by = "ident",
    plots = c("ridgeplot", "jitter"))

## Picking joint bandwidth of 0.859

# dittoDimPlot
dittoDimPlot("ident", seurat, size = 3)

dittoDimPlot("CD3E", seurat, size = 3)

# dittoBarPlot
dittoBarPlot("ident", seurat, group.by = "RNA_snn_res.0.8")

dittoBarPlot("ident", seurat, group.by = "RNA_snn_res.0.8",
    scale = "count")

# dittoHeatmap
dittoHeatmap(genes = get.genes(seurat)[1:20], seurat)

dittoHeatmap(genes = get.genes(seurat)[1:20], seurat,
    annotation.metas = c("groups", "ident"),
    scaled.to.max = TRUE,
    show.colnames = FALSE)

# Turning off cell clustering can be necessary for many cell scRNAseq
dittoHeatmap(genes = get.genes(seurat)[1:20], seurat,
    cluster_cols = FALSE)

# dittoScatterPlot
dittoScatterPlot(
    x.var = "CD3E", y.var = "nCount_RNA",
    color.var = "ident", shape.var = "RNA_snn_res.0.8",
    object = seurat,
    size = 3)

# Also:
    # multi_dittoDimPlot (multiple, in an array)
    # multi_dittoDimPlotVaryCells (multiple, in an array, but showing only certain
    #     cells in each plot)
    # multi_dittoPlot (multiple, in an array)
    # dittoPlot_VarsAcrossGroups (multiple genes or metadata as the jitterpoints (and
    #     other representations), summarized across groups by mean, median, ..., )

Many adjustments can be made with simple additional inputs:

Many adjustments to how data is reresented are within the examples above. See documentation for more! Also,

All Titles are adjustable.
Colors can be adjusted easily.
Underlying data can be output.
plotly hovering can be added.
Many more! (Legends removal, label rotation, labels’ and groupings’ names, …)

dittoBarPlot("ident", seurat, group.by = "RNA_snn_res.0.8",
    main = "Starters",
    sub = "By Type",
    xlab = NULL,
    ylab = "Generation 1",
    x.labels = c("Ash", "Misty"),
    legend.title = "Types",
    var.labels.rename = c("Fire", "Water", "Grass"),
    x.labels.rotate = FALSE)

dittoBarPlot("ident", seurat, group.by = "RNA_snn_res.0.8",
    colors = c(3,1,2)) #Just changes the color order, probably most useful for dittoDimPlots

dittoBarPlot("ident", seurat, group.by = "RNA_snn_res.0.8",
    color.panel = c("red", "orange", "purple"))

dittoBarPlot("ident", seurat, group.by = "RNA_snn_res.0.8",
    data.out = TRUE)

##   label grouping count label.count.total    percent
## 1     0        0    35                53 0.66037736
## 2     1        0     1                53 0.01886792
## 3     2        0    17                53 0.32075472
## 4     0        1     1                27 0.03703704
## 5     1        1    24                27 0.88888889
## 6     2        1     2                27 0.07407407

dittoBarPlot("ident", seurat, group.by = "RNA_snn_res.0.8",
    do.hover = TRUE)

## Warning: Ignoring unknown aesthetics: text

Render of Quick Start Guide

Daniel Bunis

11/2/2019

News: version 0.3.0 is LIVE

Color blindness friendliness:

Quick Start Guide:

There are many dittoSeq Plot Types